Monthly Archives: December 2016

Everyone is a closet technician. Everyone. And in a panic or a market correction, this truism is even more, um, truistic.

First, what is a technician? Here’s my own handy definition, I think you’ll like it: A technician is someone who cuts right to the chase and studies actual prices and behavior instead of puzzling over the causes of prices and behavior like everyone else.

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It happens each time and it is always hilarious. They’ll deny it: “The market is wrong” or “these are mispriced securities.”

The technician is one step ahead: “The market is not wrong, it has a current set of collective beliefs that are subject to change. Price will tell us when there is a likelihood that this change is at hand.”

Let’s get back to the “Why?” question and the fact that technicians don’t waste their time with it.

There’s a cognitive foible common to human beings known as the Hindsight Bias. As investors, there’s nothing we like to do more than looking back at an event that’s just taken place and reciting the reasons for what caused it as though they were obvious to us in advance. “I knew it all along! It was China, Greece, the Fed, that magazine cover, Obama, the rate hike.”

This is even more true of clients who are subject to the daily reporting of ONLY the S&P and Dow levels, and results in career risk for the investment professional. “Why isn’t my account going up as much as the S&P?” Because you have a diversified portfolio, which contains some portion of the S&P, and will sometimes do better and sometimes worse. And when the S&P is beating everything, your diversified portfolio will always be doing worse. But the responsible professional cannot recommend only the S&P just because this is the safest for keeping a client happy. The client’s immediate happiness is not the goal: his/her long term financial security is.

The trick, then, is educating the clients to understand that market movements are part of the process, and diversification is a feature, not a bug.

The beginning of the year is filled with forecasts. People love to read them, and as a practical matter, we may use them in preparing capital market assumptions, which are required for the portfolio construction process and other models we use every day. How accurate are these forward looking estimates?

William Sherden reviewed research on forecasting accuracy from 1979 to 1995. He concluded that1:

Economists cannot predict the turning points in the economy. Of the 48 predictions made by economists, 46 missed the turning points.

Economists’ forecasting skill is about as good as guessing. Even economists who directly or indirectly run the economy (the Fed, the Council of Economic Advisors and the Congressional Budget Office) had forecasting records that were worse than pure chance.

There are no economic forecasters who consistently excel in forecasting accuracy.

Consensus forecasts don’t improve accuracy.

According to Jan Hatzius, the chief economist of Goldman Sachs2:

The majority of economists didn’t “predict” the three most recent recessions (1990, 2001 and 2007) even after they had begun.

In November 2007, economists in the Philadelphia Federal Reserve’s Survey of Professional Forecasters called for growth of 2.4 percent for 2008, with only a 3 percent chance of a recession, and only a 1 in 500 chance of the GDP falling by more than 2 percent. GDP actually fell 3.3 percent.

Since 1990, economists have forecasted only two of the 60 recessions that occurred around the world a year in advance.

So we don’t see the turning points ahead of time. What about the numerical forecasts themselves? The Fed studies their own and consensus forecasts from time to time. In 2015, an analysis of several economic indicators found that using the most simple statistical models (such as a random walk, or a first order autoregressive model) equaled or outperformed the Greenbooks (the estimates used by the Fed) for all time periods modeled except less than one quarter ahead.3

It turns out that the forecasts used by the government, and “consensus” forecasts, have two main ongoing biases: recency bias and optimism.

From FOMC meeting minutes and FRED data:

The above chart shows both recency and optimism. The recency can be seen as near term estimates are quite close to the most recent year’s actual data. The optimism can be seen as every estimate comes in above actual results.

So there’s some more of the optimism. There are many examples of similar charts for other estimates as well.

This chart from Resolve Asset Management shows the recency bias, using bond yield forecasts and data. Again, the forecasts were from the Fed. Source: (Brooks & Gray, 2003)6:

The CBO also checks on its own forecasts and makes comparisons to around 50 private sector forecasts. Their most recent analysis includes the time period 1982 – 2012. The result of this analysis is that their forecasts deviated from actual outcomes by 1.4 percentage points for real GDP growth and 0.8 percentage points for inflation.7

Edward P. Lazear looked at the CBO data from 1999-2013 and found the GDP estimates to be off by 1.7 percentage points. The reason this is notable is that during that time period, the average GDP was only 2.1%. They didn’t miss by 1.7% of 2.1%, they missed by over 80% of the actual result. On average.

Lazear also found that “History is a better predictor of annual growth than government forecasts. Simply assuming that GDP growth will be 3.1% in each year—the average annual rate for the 30 years that precede the study period—results in an average forecast error of 1.5 percentage points.”8

So we know the forecasts are wrong, but we still need estimates for our models. The research above leaves us with a couple of options that are at least as accurate as the forecasts: simple statistical models, and historical averages. The simplest model may be all the complexity that is needed, and the naïve historical average may not be so naïve after all.

Tesla Terminator? What? If you read the article, you will see that Lucid Motors plans to ramp up to a staggering 60,000 cars per year production level of a car selling for over $100,000. This is not a Tesla killer, it’s a Tesla complement.

Nothing could be better, if you are Tesla, than more companies with that same electric target on their backs. As a huge Tesla fan, I see this as a win. It verifies that the Tesla business model makes sense, and will continue.

Bloomberg details the current cost of renewables vs. fossil fuels here. For new installations, solar and wind are now both cheaper than any fossil fuel power plant. What does that mean? Well, it means that no one will build new fossil fuel power plants. However, do not confuse this with “solar is cheaper now so everyone will switch.”

It’s still cheaper to increase production at existing power plants, even if that requires investment to improve environmental impacts. It’s also cheaper to retrofit existing plants to run other types of fuels, like coal to natural gas, or coal to biomass. Please note, China claims to be moving away from coal.

What we might hope for is to see the solar industry in the US benefit and grow, supplying emerging economies with energy. Although China dominates all solar power markets, the US does have significant manufacturing.

What about oil and electric cars? Bloomberg also posted this video comparing the recent oil glut and price drop to the amount of oil use expected to be eliminated as planned electric vehicle manufacturing gets into high gear. This is from February 2016, but it’s pretty good:

During the FMD (Fasting Mimicking Diet), our genomes activate autophagy or “self-eating.” Our bodies begin to look for the components that are unnecessary and nonfunctioning. They are selectively broken down and the nutrients they contain are utilized.

For example, Longo (Valter Longo, director of the USC Longevity Institute) measured T cells—the immune system’s defense against cancers and other diseases. At any one time, about a third of your T cells are old and nonfunctioning. So, they’re broken down and utilized for their nutritional content during the FMD.

When you’ve finished the FMD and are eating in modern feast mode, your stem cell processes quickly kick in and replace those missing T cells. So, you have a supercharged fully functioning immune system, perhaps for the first time. This may explain why cancer rates in animals fall after FMD, but there is another possibility.

Our mitochondria are essentially a symbiotic species of bacteria. These aliens within come entirely from your mother’s supply and have their own short circular DNA rings called plasmids. When they are working properly, they communicate with one another and the genome, creating the intelligent energy grid that powers our bodies.

Unlike T cells and other cells, mitochondria replicate through fission, splitting into additional mitochondria in the same way that bacteria replicate. This is called mitochondrial biogenesis and it is critical to life.

Like T cells, mitochondria get old. This is a major problem because mitochondria function like batteries. On one side of an inner membrane is an area of low ion concentration. On the other side is a higher concentration. This difference in ion levels powers the production of the energy molecules our bodies require: adenosine triphosphate.

The problem is that old mitochondria leak. Ions escape and result in the formation of free radicals or reactive oxygen species. Though free radicals play necessary and important biological roles, the unintended production of these molecules is responsible for a great deal of accelerated aging and disease.

This view is reinforced by research showing that calorie restriction activates the creation of new mitochondria, presumably to replace those that have been cleared out by mitophagy. This may also explain why short-term FMD does not lead to a loss of muscle tissue, which is an unfortunate side effect of long-term dieting.